English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Assortment optimization is an important problem that arises in manyindustries such as retailing and online advertising where the goal is to find asubset of products from a universe of substitutable products which maximizeseller's expected revenue. One of the key challenges in this problem is tomodel the customer substitution behavior. Many parametric random utilitymaximization (RUM) based choice models have been considered in the literature.However, in all these models, probability of purchase increases as we includemore products to an assortment. This is not true in general and in manysettings more choices hurt sales. This is commonly referred to as the choiceoverload. In this paper we attempt to address this limitation in RUM through ageneralization of the Markov chain based choice model considered in Blanchet etal. (2016). As a special case, we show that our model reduces to ageneralization of MNL with no-purchase attractions dependent on the assortmentS and strictly increasing with the size of assortment S. While we show that theassortment optimization under this model is NP-hard, we present fullypolynomial-time approximation scheme (FPTAS) under reasonable assumptions.

A Generalized Markov Chain Model to Capture Dynamic Preferences and Choice Overload